什么是 Featuretools？

Featuretools 是一个执行自动化特征工程的框架。它擅长将时态和关系数据集转换为用于机器学习的特征矩阵。

5 分钟快速入门

下面是一个使用深度特征合成 (DFS) 执行自动化特征工程的示例。在此示例中，我们将 DFS 应用于由带时间戳的客户交易组成的多表数据集。

import featuretools as ft

加载模拟数据

data = ft.demo.load_mock_customer()

准备数据

在这个模拟数据集中，有 3 个 DataFrame。

• customers：进行过会话的唯一客户

• sessions：唯一的会话和相关属性

• transactions：此会话中的事件列表

customers_df = data["customers"]
customers_df

	customer_id	zip_code	join_date	birthday
0	1	60091	2011-04-17 10:48:33	1994-07-18
1	2	13244	2012-04-15 23:31:04	1986-08-18
2	3	13244	2011-08-13 15:42:34	2003-11-21
3	4	60091	2011-04-08 20:08:14	2006-08-15
4	5	60091	2010-07-17 05:27:50	1984-07-28

sessions_df = data["sessions"]
sessions_df.sample(5)

	session_id	customer_id	device	session_start
13	14	1	tablet	2014-01-01 03:28:00
6	7	3	tablet	2014-01-01 01:39:40
1	2	5	mobile	2014-01-01 00:17:20
28	29	1	mobile	2014-01-01 07:10:05
24	25	3	desktop	2014-01-01 05:59:40

transactions_df = data["transactions"]
transactions_df.sample(5)

	transaction_id	session_id	transaction_time	product_id	amount
74	417	5	2014-01-01 01:20:10	1	139.20
231	229	17	2014-01-01 04:10:15	2	90.79
434	127	31	2014-01-01 07:50:10	3	62.35
420	359	30	2014-01-01 07:35:00	3	72.70
54	249	4	2014-01-01 00:58:30	4	43.59

首先，我们指定一个包含数据集中所有 DataFrame 的字典。如果 DataFrame 存在索引列和时间索引列，则将其一并传入。

dataframes = {
    "customers": (customers_df, "customer_id"),
    "sessions": (sessions_df, "session_id", "session_start"),
    "transactions": (transactions_df, "transaction_id", "transaction_time"),
}

其次，我们指定 DataFrame 如何关联。当两个 DataFrame 具有一对多关系时，我们将“一”的那一方 DataFrame 称为“父 DataFrame”。父子关系定义如下

(parent_dataframe, parent_column, child_dataframe, child_column)

在此数据集中，我们有两种关系

relationships = [
    ("sessions", "session_id", "transactions", "session_id"),
    ("customers", "customer_id", "sessions", "customer_id"),
]

注意

为了管理 DataFrame 和关系的设置，我们建议使用 EntitySet 类，该类提供了方便的 API 来管理此类数据。有关更多信息，请参阅 使用 EntitySet 表示数据。

运行深度特征合成

DFS 的最小输入是一个 DataFrame 字典、一个关系列表以及我们要计算其特征的目标 DataFrame 名称。DFS 的输出是一个特征矩阵和相应的特征定义列表。

首先，让我们为数据中的每个客户创建一个特征矩阵

feature_matrix_customers, features_defs = ft.dfs(
    dataframes=dataframes,
    relationships=relationships,
    target_dataframe_name="customers",
)
feature_matrix_customers

/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function std at 0x7f2a84110940> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function mean at 0x7f2a84110820> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function sum at 0x7f2a8410b8b0> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function max at 0x7f2a8410bee0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function min at 0x7f2a84110040> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function sum at 0x7f2a8410b8b0> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function mean at 0x7f2a84110820> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function max at 0x7f2a8410bee0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function std at 0x7f2a84110940> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function min at 0x7f2a84110040> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function max at 0x7f2a8410bee0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function std at 0x7f2a84110940> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function sum at 0x7f2a8410b8b0> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function mean at 0x7f2a84110820> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function min at 0x7f2a84110040> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function max at 0x7f2a8410bee0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function std at 0x7f2a84110940> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function mean at 0x7f2a84110820> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function min at 0x7f2a84110040> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function sum at 0x7f2a8410b8b0> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function sum at 0x7f2a8410b8b0> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function std at 0x7f2a84110940> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function mean at 0x7f2a84110820> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function max at 0x7f2a8410bee0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function min at 0x7f2a84110040> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  to_merge = base_frame.groupby(

	zip_code	COUNT(sessions)	MODE(sessions.device)	NUM_UNIQUE(sessions.device)	COUNT(transactions)	MAX(transactions.amount)	MEAN(transactions.amount)	MIN(transactions.amount)	MODE(transactions.product_id)	NUM_UNIQUE(transactions.product_id)	...	STD(sessions.SKEW(transactions.amount))	STD(sessions.SUM(transactions.amount))	SUM(sessions.MAX(transactions.amount))	SUM(sessions.MEAN(transactions.amount))	SUM(sessions.MIN(transactions.amount))	SUM(sessions.NUM_UNIQUE(transactions.product_id))	SUM(sessions.SKEW(transactions.amount))	SUM(sessions.STD(transactions.amount))	MODE(transactions.sessions.device)	NUM_UNIQUE(transactions.sessions.device)
customer_id
1	60091	8	mobile	3	126	139.43	71.631905	5.81	4	5	...	0.589386	279.510713	1057.97	582.193117	78.59	40.0	-0.476122	312.745952	mobile	3
2	13244	7	desktop	3	93	146.81	77.422366	8.73	4	5	...	0.509798	251.609234	931.63	548.905851	154.60	35.0	-0.277640	258.700528	desktop	3
3	13244	6	desktop	3	93	149.15	67.060430	5.89	1	5	...	0.429374	219.021420	847.63	405.237462	66.21	29.0	2.286086	257.299895	desktop	3
4	60091	8	mobile	3	109	149.95	80.070459	5.73	2	5	...	0.387884	235.992478	1157.99	649.657515	131.51	37.0	0.002764	356.125829	mobile	3
5	60091	6	mobile	3	79	149.02	80.375443	7.55	5	5	...	0.415426	402.775486	839.76	472.231119	86.49	30.0	0.014384	259.873954	mobile	3

5 行 × 75 列

我们现在有几十个新特征来描述客户的行为。

更改目标 DataFrame

DFS 如此强大的原因之一是它可以为 EntitySet 中的任何 DataFrame 创建特征矩阵。例如，如果我们想为会话构建特征。

feature_matrix_sessions, features_defs = ft.dfs(
    dataframes=dataframes, relationships=relationships, target_dataframe_name="sessions"
)
feature_matrix_sessions.head(5)

/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function sum at 0x7f2a8410b8b0> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function std at 0x7f2a84110940> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function min at 0x7f2a84110040> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function mean at 0x7f2a84110820> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function max at 0x7f2a8410bee0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function mean at 0x7f2a84110820> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function max at 0x7f2a8410bee0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function min at 0x7f2a84110040> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function std at 0x7f2a84110940> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
  to_merge = base_frame.groupby(
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/stable/lib/python3.9/site-packages/featuretools/computational_backends/feature_set_calculator.py:781: FutureWarning: The provided callable <function sum at 0x7f2a8410b8b0> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
  to_merge = base_frame.groupby(

	customer_id	device	COUNT(transactions)	MAX(transactions.amount)	MEAN(transactions.amount)	MIN(transactions.amount)	MODE(transactions.product_id)	NUM_UNIQUE(transactions.product_id)	SKEW(transactions.amount)	STD(transactions.amount)	...	customers.STD(transactions.amount)	customers.SUM(transactions.amount)	customers.DAY(birthday)	customers.DAY(join_date)	customers.MONTH(birthday)	customers.MONTH(join_date)	customers.WEEKDAY(birthday)	customers.WEEKDAY(join_date)	customers.YEAR(birthday)	customers.YEAR(join_date)
session_id
1	2	desktop	16	141.66	76.813125	20.91	3	5	0.295458	41.600976	...	37.705178	7200.28	18	15	8	4	0	6	1986	2012
2	5	mobile	10	135.25	74.696000	9.32	5	5	-0.160550	45.893591	...	44.095630	6349.66	28	17	7	7	5	5	1984	2010
3	4	mobile	15	147.73	88.600000	8.70	1	5	-0.324012	46.240016	...	45.068765	8727.68	15	8	8	4	1	4	2006	2011
4	1	mobile	25	129.00	64.557200	6.29	5	5	0.234349	40.187205	...	40.442059	9025.62	18	17	7	4	0	6	1994	2011
5	4	mobile	11	139.20	70.638182	7.43	5	5	0.336381	48.918663	...	45.068765	8727.68	15	8	8	4	1	4	2006	2011

5 行 × 44 列

理解特征输出

通常，Featuretools 通过特征名称引用生成的特征。为了使特征更易于理解，Featuretools 提供了另外两个工具，featuretools.graph_feature() 和 featuretools.describe_feature()，以帮助解释特征是什么以及 Featuretools 生成它的步骤。让我们看看这个示例特征

feature = features_defs[18]
feature

<Feature: MODE(transactions.WEEKDAY(transaction_time))>

特征血缘图

特征血缘图通过可视化方式展示特征生成过程。从基础数据开始，它们一步一步地展示应用的基元以及生成的中间特征，最终创建出最终特征。

ft.graph_feature(feature)

特征描述

Featuretools 还可以自动生成特征的英文句子描述。特征描述有助于解释特征是什么，并且可以通过包含手动定义的自定义定义来进一步改进。有关如何自定义自动生成特征描述的更多详细信息，请参阅 生成特征描述。

ft.describe_feature(feature)

'The most frequently occurring value of the day of the week of the "transaction_time" of all instances of "transactions" for each "session_id" in "sessions".'

下一步是什么？

• 了解 使用 EntitySet 表示数据

• 使用 深度特征合成 应用自动化特征工程

• 探索基于实际用例的可运行示例

• 找不到您要查找的内容？请求帮助

目录

• 安装
• 开发

• 入门
  • 使用 EntitySet 表示数据
  • 深度特征合成
  • 特征基元
  • Featuretools 中的 Woodwork 类型化
  • 处理时间
  • 什么是截止时间？
• 指南
  • 调整深度特征合成
  • 指定基元选项
  • 提高计算性能
  • 部署
  • 高级自定义基元指南
  • 生成特征描述
  • 特征选择
  • 时间序列问题的特征工程
  • SQL 数据库集成

资源与参考

• 资源
• API 参考
• 发布说明

其他链接

• 索引

• 搜索页面